The present invention relates to communications methods and apparatus, and more particularly, to methods and apparatus for receiving communications signals subject to noise such as those typically found in wireless communication systems. Wireless communications systems are commonly employed to provide voice and data communications to subscribers. For example, analog cellular radiotelephone systems, such as those designated AMPS, ETACS, NMT-450, and NMT-900, have long been deployed successfully throughout the world. Digital cellular radiotelephone systems such as those conforming to the North American standard IS-54 and the European standard GSM have been in service since the early 1990""s. More recently, a wide variety of wireless digital services broadly labeled as PCS (Personal Communications Services) have been introduced, including advanced digital cellular systems conforming to standards such as IS-136 and IS-95, lower-power systems such as DECT (Digital Enhanced Cordless Telephone) and data communications services such as CDPD (Cellular Digital Packet Data). These and other systems are described in The Mobile Communications Handbook, edited by Gibson and published by CRC Press (1996).
Wireless communications systems such as cellular radiotelephone systems typically include a plurality of communication channels which may be established between a first transceiver (such as a base station) and a second transceiver (such as a mobile terminal). The communication channels typically are subject to performance-degrading environmental effects such as multi-path fading and interference (noise). Fading effects include flat fading which may arise from the interaction of a transmitted signal (the main ray) with reflected versions of the transmitted signal that arrive concurrently at a receiver. Time dispersion, another type of fading, may arise from interaction of the main ray with time-delayed reflections of the main ray. Interference effects may be caused by interaction of non-orthogonal signals generated in the signal medium by sources other than the source of the desired transmitted signal. These various sources of signal disturbances may come from a variety of sources including thermal noise, a co-channel interferer and an adjacent-channel interferer. Most cellular communication standards typically require the receiver to achieve a minimum adjacent-channel protection (ACP). Unfortunately, to meet this minimum specification, a narrow receive filter is often used in the receiver at the expense of losing co-channel performance which might otherwise be obtainable with a wider receive filter.
The dynamic characteristics of the radio channel present difficulties in tracking the channel to allow for decoding of information contained in the received signal. Often, in wireless mobile radio systems, known data sequences are inserted periodically into the transmitted information sequences. Such data sequences are commonly called synchronizing sequences or training sequences and are typically provided at the beginning or in the middle of a frame of data or a burst of data. Channel estimation may be carried out using the synchronizing sequences and other known parameters to estimate the impact the channel has on the transmitted signal. Least square estimation may be an efficient way of estimating the channel impulse response in the presence of additive Gaussian (white) noise. However, as the noise becomes non-white, or colored, these techniques may become less effective.
To extract the transmitted signal (or symbols) from the received signal, the receiver of a mobile terminal typically includes a demodulator which may be a coherent demodulator such as a maximum likelihood sequence estimation (MLSE) demodulator (or equalizer). To adapt to the channel variation over each data burst, an associated channel tracker is typically provided for the demodulator. The channel tracker typically operates in a xe2x80x9cdecision directedxe2x80x9d mode where the symbol estimates are used to track the variations of the channel. After acquisition of a communicated signal by the receiver, the channel tracker maintains a channel estimate to provide a coherent reference between the demodulator and the received signal. The most commonly used channel tracking methods are the Least Mean Square (LMS) and Recursive Least Square (RLS) based algorithms. See for example, xe2x80x9cOptimal Tracking of Time-varying Channels: A Frequency Domain Approach for known and new algorithms,xe2x80x9d IEEE Transactions on Selected Areas in Communications, Vol. 13, NO. 1, January 1995, Jingdong Lin, John G. Proakis, Fuyun Ling.
At any given time, the kind of disturbances (co-channel interferences, adjacent-channel interference, or thermal noise) that dominates in the received signal is generally unknown. The typical approach is to design the demodulator or the equalizer in the receiver assuming the dominant disturbance is white (i.e. uncorrelated in time), hoping that it will suffice well even when the disturbance is somewhat colored.
For example, consider the receiver model depicted in FIG. 1. A signal y(t) is first filtered in an analog filter 105 having a transfer function h(t) to provide a received signal r(t) which is downsampled to a symbol rate received signal r(n) before processing in the equalizer 110 to get a signal estimate Sest(u). As used herein, the term xe2x80x9csymbol ratexe2x80x9d encompasses both the symbol transmission rate and multiples thereof. The symbol-rate downsampled discrete-time received signal r(n) is given by:                                           r            ⁡                          (              n              )                                =                                                    ∑                                  i                  =                  0                                                  L                  -                  1                                            ⁢                              xe2x80x83                            ⁢                                                c                  ⁡                                      (                    i                    )                                                  ⁢                                  s                  ⁡                                      (                                          n                      -                      i                                        )                                                                        +                          w              ⁡                              (                n                )                                                    ,                            (        1        )            
where c(i) is the discrete-time based-band channel model with L coefficients, s is the transmitted symbols, and w(n) is a discrete-time random process caused by the signal disturbance (this sequence can be either colored or white and may be referred to as noise).
At a time xe2x80x9cn xe2x80x9d, each state in the trellis of a maximum likelihood sequence estimation (MLSE) equalizer can be expressed as Sn=[s(n),s(nxe2x88x921), . . . , s(nxe2x88x92L+2)]. At each state of the trellis, a surviving path and a cumulative path metric M(Sn) are kept for each of the 8Lxe2x88x921 states. Also, at each stage of the trellis, the branch metric is:                               dM          ⁡                      (                                          S                n                            ,                              S                                  n                  -                  1                                                      )                          =                              "LeftBracketingBar"                                          r                ⁡                                  (                  n                  )                                            -                                                ∑                                      i                    =                    0                                                        L                    -                    1                                                  ⁢                                  xe2x80x83                                ⁢                                                      c                    ⁡                                          (                      i                      )                                                        ⁢                                                            s                      ^                                        ⁡                                          (                                              n                        -                        1                                            )                                                                                            "RightBracketingBar"                    2                                    (        2        )            
where ŝ is the signal (symbol) estimate and dM(Sn,Snxe2x88x921)corresponding to the state transition from one previous hypothesized state, Snxe2x88x921, to the current hypothesized state, Sn, which is computed and added to the path metric M(Snxe2x88x921) associated with the previous state. The path metric of the current state may then be updated by choosing the minimum of the accumulated metrics among all paths that terminate in the current hypothesized state, Sn.
Equation (2) implicitly assumes that w(n) is a white Gaussian sequence (i.e. it assumes that the w(n)s are uncorrelated in time). However, in many practical cases where the dominant disturbance is not the thermal noise, this assumption is not valid. Even when the disturbance is just the thermal noise, w(n) may not be white if the receive filter 105 h(t) is not Nyquist. In this case, however, the autocorrelation of w(n) denoted by xcfx81ww(m), is typically fixed and can be found by:
xcfx81ww(m)=E[w(n)w*(nxe2x88x92m)]=N0∫h(t) h*(txe2x88x92mT)dt,xe2x80x83xe2x80x83(3)
where N0=E[|n(t)|2].
Typically, given any colored stationary sequence v(n), one can design a casual, invertible, linear and time-invariant (LTI) whitening filter with input v(n) and output z(n), where z(n) is white. As the whitening filter is generally causal and invertible, this filter typically does not cause any loss in information. This whitening filter is closely related to the linear least-squares one-step predictor of v(n). Specifically, let                                           w            ^                    ⁡                      (                          n              |                              n                -                1                                      )                          =                              ∑                          i              =              1                        ∞                    ⁢                      xe2x80x83                    ⁢                                    a              ⁡                              (                i                )                                      ⁢                          w              ⁡                              (                                  n                  -                  i                                )                                                                        (        4        )            
be the linear least-squares predictor of w(n) based on {w(m) m less than n}. As described in A. Papoulis, Probability, Random Variables, and Stochastic Processor, McGraw-Hill, 1984, the impulse response of the whitening filter for v(n) is:
h(n)=xe2x88x92a(n) for nxe2x89xa71xe2x80x83xe2x80x83(5)
and h(n)=1 for n=0 and h(n)=0 for n less than 0. Furthermore:                               z          ⁡                      (            n            )                          =                              w            ⁡                          (              n              )                                *                      h            ⁡                          (              n              )                                                                    xe2x80x83                ⁢                  (          6          )                                        =                              w            ⁡                          (              n              )                                -                                                    w                ^                            ⁡                              (                                  n                  |                                      n                    -                    1                                                  )                                      .                                                        xe2x80x83                ⁢                  (          7          )                    
Using the orthogonality principle of linear estimation, it may be shown that z(n) in equation (7) is a white (i.e. uncorrelated) sequence; hence, h(n) is the desired whitening filter. Using recursion, it is also possible to show that h(n) is casually invertible; hence, h(n) is minimum-phase.
This approach to whitening the disturbances is suggested in David Forney, xe2x80x9cMaximum-Likelihood Sequence Estimation of Digital Sequences in the Presence of Intersymbol Interference,xe2x80x9d Info. Theory, 1972. The Forney article discusses undoing the effect of noise coloring caused by the matched filter by adding a digital noise whitening filter between the sampler and the equalizer (an MLSE equalizer with a Euclidean metric). The desired impulse response of the whitening filter can be computed using the autocorrelation function of
w(n)(i.e. xcfx81ww(m)=E{w(n)w*(nxe2x88x92m)}).
The above approach may work when the input signal disturbance is white (for example, thermal noise), as, in this case, xcfx81ww(m)=E{w(n) w* (nxe2x88x92m)} is typically fixed and known from equation (3) for each lag m. However, when the disturbance is dominated by co-channel or adjacent-channel interference, it is believed that the autocorrelation of w(n) would need to be estimated for all lags in order to compute {a(i)} properly. In addition, the effective baseband channel seen by the equalizer will generally consist of the convolution of the whitening filter and the original channel go Therefore, the equalizer may have to equalize a much longer channel, possibly requiring a more complex equalizer.
According to the present invention, methods, systems and receiver devices are provide which may reduce the average power of a signal disturbance by whitening the signal disturbance. In one aspect, a finite impulse response filter (FIR) is provided which whitens the signal disturbance by filtering a downsampled received signal using filter coefficients adaptively established using known signal information in each signal burst of the received signal. Alternatively, a noise-whitening equalizer is utilized having a modified metric that whitens the signal disturbance again using coefficients adaptively established using known signal information in each signal burst of the received signal. The noise-whitening equalizer approach further allows the noise-whitening coefficients to be updated by treating symbol estimates from the equalizer as known signal information to generate updated noise-whitening coefficients. A novel receiver containing a modified Euclidean metric equalizer to provide noise-whitening is also provided.
In one embodiment of the present invention, a method is provided for adaptively whitening a signal disturbance in a communication signal the communication signal, which includes the signal disturbance, is received and coefficients are determined for whitening the signal disturbance using known signal information from the received signal. An estimate of the transmitted signal (or symbols), referred to herein as a xe2x80x9csignal estimatexe2x80x9d of a xe2x80x9csymbol estimatexe2x80x9d, is generated for the received signal using the determined coefficients. The communication signal may include a plurality of signal bursts containing the known signal information which are received. The steps of receiving, determining and generating are repeated for at least two of the plurality of signal bursts. Accordingly, the determined coefficients are updated for each burst and then used by the equalizer in generating the signal estimates for the received burst which was used to determine (update) the coefficients.
In a further embodiment of the present invention, the signal estimate is generating by processing the received signal through a whitening filter having a selected number of taps associated with the determined coefficients and processing the filtered received signal through an equalizer to generate a signal estimate for the received signal. The communication signal is preferably downsampled to a symbol rate of the communication signal before filtering. In one embodiment, the received signal is processed through a whitening filter having M+1 taps, where M is a selected integer, and wherein the determined coefficients are coefficients of the whitening filter which are based on an M-th order linear predictor of the signal disturbance. The coefficients of the whitening filter may be determined over a training sequence in the received signal.
In another embodiment of the present invention, the coefficients for whitening the signal disturbance are determined by first determining a plurality of channel taps of the equalizer and determining the signal disturbance based on the determined channel taps and samples of the received signal. An auto-correlation of the signal disturbance is determined for a plurality of lags and the coefficients of the whitening filter are established based on the determined auto-correlation of the signal disturbance. The whitening filter may be a minimum-phase filter. The whitening filter may also be monic with simple scaling. The equalizer may use a Euclidean metric.
In further embodiment of the present invention, the signal estimate for the received signal is generated by processing the received signal through a noise-whitening equalizer to generate a signal estimate for the received signal. Metrics of the noise-whitening equalizer are modified to convert the signal disturbance to a substantially white noise signal disturbance. An estimate of the signal disturbance may be maintained for a plurality of states in a decoding trellis of the noise-whitening equalizer.
In yet another embodiment of the present invention, the coefficients for whitening the signal disturbance are determined by first determining a plurality of channel taps of the noise-whitening equalizer and determining the signal disturbance based on the determined channel taps and samples of the received signal. An auto-correlation of the signal disturbance is determined for a plurality of lags to provide a model for the color of the signal disturbance. The received signal is processed through a noise-whitening equalizer having a coefficients determined based on the model for the color of the signal disturbance to generate a signal estimate for the received signal. The noise-whitening equalizer may be an Ungerboeck maximum likelihood sequence estimation (MLSE) equalizer.
In a further embodiment of the present invention, the determined coefficients are updated based on the signal estimate from the noise-whitening equalizer. The update may be provided by treating the signal estimate from the noise-whitening equalizer as known signal information.
In further aspect of the present invention, a method for whitening a signal disturbance in a received signal is provided. The received signal, wherein r(n) is the received signal at an nth symbol period, is processed through a modified Euclidean metric equalizer using metrics defined by the equation   dM  =            "LeftBracketingBar"                        r          ⁡                      (            n            )                          -                              ∑                          i              =              0                                      L              -              1                                ⁢                      xe2x80x83                    ⁢                                    c              ⁡                              (                i                )                                      ⁢                                          s                ^                            ⁡                              (                                  n                  -                  i                                )                                                    -                              w            ^                    ⁡                      (                          n              |                              n                -                1                                      )                              "RightBracketingBar"        2  
where c(i) is a channel estimate, ŝ (nxe2x88x92i) is a symbol estimate, L is a number of coefficients in the channel estimate (or model), and ŵ(n|nxe2x88x921) is a one-step ahead predictor of the signal disturbance at the nth symbol which is based on estimated characteristics of the signal disturbance to generate a signal estimate for the received signal.
In an apparatus aspect of the present invention, a receiver device is provided including a receiver that receives wireless communication signals including a signal disturbance and downsamples the received signals to a symbol rate of the communication signals to provide received signal samples. The receiver device also includes a noise-whitening filter that filters the received signal samples, the noise-whitening filter having filter coefficients. An equalizer generates symbol estimates from the filtered received signal samples. The receiver device also includes a filter coefficient estimation circuit that generates the filter coefficients based on known signal information from the received signals.
In a further embodiment of the present invention, a receiver device is provided including a receiver that receives wireless communication signals including a signal disturbance and downsamples the received signals to a symbol rate of the communication signals to provide received signal samples. The receiver device further includes a noise-whitening equalizer having an associated metric that generates a signal estimate for the received signal and a metric circuit that adjusts the metric based on known signal information from the received signals. The metric circuit preferably outputs a model of the signal disturbance to the noise-whitening equalizer and the associated metric is based on the model of the signal disturbance.
In yet another embodiment of the present invention, a receiver device is provided including a receiver that receives wireless communication signals including a signal disturbance and downsamples the received signals to a symbol rate of the communication signals to provide received signal samples, where r(n) is a received signal sample at an nth symbol period. The receiver device further includes a modified Euclidean metric equalizer using metrics defined by the equation   dM  =            "LeftBracketingBar"                        r          ⁡                      (            n            )                          -                              ∑                          i              =              0                                      L              -              1                                ⁢                      xe2x80x83                    ⁢                                    c              ⁡                              (                i                )                                      ⁢                                          s                ^                            ⁡                              (                                  n                  -                  i                                )                                                    -                              w            ^                    ⁡                      (                          n              |                              n                -                1                                      )                              "RightBracketingBar"        2  
where c(i) is a channel estimate, ŝ (nxe2x88x92i) is a symbol estimate, L is a number of coefficients in the channel estimate (or model), and ŵ(n|nxe2x88x921) is a one-step ahead predictor of the signal disturbance at the nth symbol which is based on estimated characteristics of the signal disturbance to generate a signal estimate for the received signal.
As will further be appreciated by those of skill in the art, while described above primarily with reference to method aspects, the present invention may also be embodied as systems.